Conference Proceeding Article
Embedding deals with reducing the high-dimensional representation of data into a low-dimensional representation. Previous work mostly focuses on preserving similarities among objects. Here, not only do we explicitly recognize multiple types of objects, but we also focus on the ordinal relationships across types. Collaborative Ordinal Embedding or COE is based on generative modelling of ordinal triples. Experiments show that COE outperforms the baselines on objective metrics, revealing its capacity for information preservation for ordinal data.
Numerical Analysis and Scientific Computing
Data Management and Analytics
Proceedings of the 2016 SIAM International Conference on Data Mining
City or Country
LE, Dung D. and LAUW, Hady Wirawan.
Euclidean co-embedding of ordinal data for multi-type visualization. (2016). Proceedings of the 2016 SIAM International Conference on Data Mining. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3358
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